# General Settings Pick
n_rounds = 1
n_summary = 9
n_samples = 10
n_hiddens = [200, 200]
n_components = 1
dt = 0.01
# Get current
I, t, t_on, t_off = syn_current(duration=70,
dt=0.01,
t_on=15,
t_off=20,
amp=3.1)
params, labels = obs_params()
params[0] *= 10
print(params)
# Set up themodel
dap = DAPcython(-75, params)
U = dap.simulate(dt, t, I)
# generate data format for SNPE / OBSERVABLE
x_o = {'data': U, 'time': t, 'dt': dt, 'I': I}
# Prior
# Setup Priors
prior_min = np.array([0, 1])
prior_max = np.array([2, 30])
prior_unif = Uniform(lower=prior_min, upper=prior_max)
# General Settings Pick
n_rounds = 1
n_summary = 17
n_samples = 5
n_hiddens = [15]
n_components = 1
dt = 0.01
reg_lambda = 0.01
n_params = 2
# Load the current
data_dir = '/home/alteska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat' # best cell
I, v, t, t_on, t_off, dt = load_current(data_dir, protocol='IV', ramp_amp=1)
params, labels = obs_params(reduced_model=True)
print(params)
print(labels)
# Set up themodel
dap = DAPcython(-75, params)
U = dap.simulate(dt, t, I)
# generate data format for SNPE / OBSERVABLE
x_o = {'data': U.reshape(-1),
'time': t,
'dt': dt,
'I': I}
# Setup Priors
prior_min, prior_max, labels = load_prior_ranges(n_params)
import glob
import shutil
import pandas as pd
from tqdm import tqdm
from scipy.spatial import distance
from dap import DAPcython
from dap.utils import obs_params, load_current
from utils import calc_features_ramp, calc_features_step
import warnings
warnings.filterwarnings("ignore")
dt = 1e-2
params, labels = obs_params(reduced_model=False)
data_dir = '/home/alteska/Desktop/LFI_DAP/data/rawData/2015_08_26b.dat'
# load the file
directory = './parameters/'
dir = glob.glob(directory + '*')
fname_start = dir[0].find('dap_')
fname_stop = dir[0].find('n_')
fname = dir[0][fname_start:fname_stop]
df_param = pd.read_csv(fname + '.csv')
df_param.set_index('Unnamed: 0', inplace=True)
# calculate DAP
# load the input data
Ir, vr, tr, t_onr, t_offr, dtr = load_current(data_dir,
